The so-called “third generation” of neural networks, SNNs offer a promising way of encoding time information, although the dynamics are hardly new ([1]}), and what constitutes a "spiking neural network" is broad, as well as the desired goal of biological plausibility or classifying power. SNNs remain at this point a loose collection of different neural-like networks that share the common feature that individual neurons operate as dynamical systems that individually collect voltage and “spike” an output at a given threshold, but differ in their layout and learning rules. SNN activation functions for the neuron vary widely, from neurons that simply integrate over time without any voltage leak ([2]}) to those that model ion channels at individual compartments along the neuron's dendrite ([3]}). Analysis of the interconnection of these spiking units in larger networks, however, is a more recent undertaking ([4]}). Efforts have been made to show that backpropagation can be implemented with SNNs as well ([5]}), although this departs from the biological plausibility and principles of self-organization. Here we analyze the information-encoding potential of a semi-recurrent feed-forward cortical-like deep network of spiking neurons, and investigate whether information is stored in assemblies as Hebb postulated. We take the approach of using a network that has solid biological basis while trying to keep the operations as simple as possible.
